Bottom Line:
Our study presents a novel 2D-to-3D video conversion algorithm which can be adopted in a 3D image sensor.In our algorithm, a depth map is generated by combining global depth gradient and local depth refinement for each frame of 2D video input.Global depth gradient is computed according to image type while local depth refinement is related to color information.

ABSTRACTIn this paper, we propose a novel 2D-to-3D video conversion method for 3D entertainment applications. 3D entertainment is getting more and more popular and can be found in many contexts, such as TV and home gaming equipment. 3D image sensors are a new method to produce stereoscopic video content conveniently and at a low cost, and can thus meet the urgent demand for 3D videos in the 3D entertaiment market. Generally, 2D image sensor and 2D-to-3D conversion chip can compose a 3D image sensor. Our study presents a novel 2D-to-3D video conversion algorithm which can be adopted in a 3D image sensor. In our algorithm, a depth map is generated by combining global depth gradient and local depth refinement for each frame of 2D video input. Global depth gradient is computed according to image type while local depth refinement is related to color information. As input 2D video content consists of a number of video shots, the proposed algorithm reuses the global depth gradient of frames within the same video shot to generate time-coherent depth maps. The experimental results prove that this novel method can adapt to different image types, reduce computational complexity and improve the temporal smoothness of generated 3D video.

Mentions:
After the image type of input frame is determined, global depth gradient is generated accordingly. For the landscape type, the upper part of the image is the sky and the lower part is the ground or water. Cumulative horizontal edge histogram [11] is used to assign global depth gradient for the following reason: As cumulative horizontal edge histogram represents the horizontal edge complexity and the sky is often smoother than the ground or water, there is a distinct depth change between the sky and the ground or water in an image of landscape type. Besides, as the global depth gradient is roughly far-to-near from top to bottom, it can be assigned 0 to 255 from top to bottom by a normalizing cumulative horizontal edge histogram. Figure 6 shows an image of landscape type and its global depth gradient.

Mentions:
After the image type of input frame is determined, global depth gradient is generated accordingly. For the landscape type, the upper part of the image is the sky and the lower part is the ground or water. Cumulative horizontal edge histogram [11] is used to assign global depth gradient for the following reason: As cumulative horizontal edge histogram represents the horizontal edge complexity and the sky is often smoother than the ground or water, there is a distinct depth change between the sky and the ground or water in an image of landscape type. Besides, as the global depth gradient is roughly far-to-near from top to bottom, it can be assigned 0 to 255 from top to bottom by a normalizing cumulative horizontal edge histogram. Figure 6 shows an image of landscape type and its global depth gradient.

Bottom Line:
Our study presents a novel 2D-to-3D video conversion algorithm which can be adopted in a 3D image sensor.In our algorithm, a depth map is generated by combining global depth gradient and local depth refinement for each frame of 2D video input.Global depth gradient is computed according to image type while local depth refinement is related to color information.

ABSTRACTIn this paper, we propose a novel 2D-to-3D video conversion method for 3D entertainment applications. 3D entertainment is getting more and more popular and can be found in many contexts, such as TV and home gaming equipment. 3D image sensors are a new method to produce stereoscopic video content conveniently and at a low cost, and can thus meet the urgent demand for 3D videos in the 3D entertaiment market. Generally, 2D image sensor and 2D-to-3D conversion chip can compose a 3D image sensor. Our study presents a novel 2D-to-3D video conversion algorithm which can be adopted in a 3D image sensor. In our algorithm, a depth map is generated by combining global depth gradient and local depth refinement for each frame of 2D video input. Global depth gradient is computed according to image type while local depth refinement is related to color information. As input 2D video content consists of a number of video shots, the proposed algorithm reuses the global depth gradient of frames within the same video shot to generate time-coherent depth maps. The experimental results prove that this novel method can adapt to different image types, reduce computational complexity and improve the temporal smoothness of generated 3D video.